End-to-end automated cache-timing attack driven by machine learning

Cache-timing attacks are serious security threats that exploit cache memories to steal secret information. We believe that the identification of a sequence of function calls from cache-timing data measurements is not a trivial step when building an attack. We present a recurrent neural network model able to automatically retrieve a sequence of operations from cache timings. Inspired from natural language processing, our model is able to learn on partially labelled data. We use the model to unfold an end-to-end automated attack on OpenSSL ECDSA on the secp256k1 curve. Our attack is able to extract the 256 bits of the secret key by automatic analysis of about 2400 traces without any human processing.